A database optimized for storing and querying high-dimensional vector embeddings using similarity search (cosine distance, dot product, Euclidean distance). Examples: Pinecone, Weaviate, Qdrant, ChromaDB, pgvector. Vector databases power RAG systems by quickly finding the most relevant documents for a given query embedding. Essential for AI-powered developer tools and documentation search.
Start with the shortest useful explanation before going deeper.
A database optimized for storing and querying high-dimensional vector embeddings using similarity search (cosine distance, dot product, Euclidean distance). Examples: Pinecone, Weaviate, Qdrant, ChromaDB, pgvector. Vector databases power RAG systems by quickly finding the most relevant documents for a given query embedding. Essential for AI-powered developer tools and documentation search.
Use the quick analogy first so the term is easier to reason about when you meet it in code, docs, or prompts.
Think of it as a piece of the context or inference stack behind agentic and LLM-powered Solana products.
Place the term inside its Solana layer so the definition is easier to reason about.
LLMs, RAG, embeddings, inference, and agent-facing primitives.
Turn the term from vocabulary into something operational for product and engineering work.
Most useful when you are moving through Agents Builder Path and need grounded vocabulary inside a real build flow.
Use this compact block when you want to give an agent or assistant grounded context without dumping the entire page.
Vector Database (vector-database) Category: AI / ML Definition: A database optimized for storing and querying high-dimensional vector embeddings using similarity search (cosine distance, dot product, Euclidean distance). Examples: Pinecone, Weaviate, Qdrant, ChromaDB, pgvector. Vector databases power RAG systems by quickly finding the most relevant documents for a given query embedding. Essential for AI-powered developer tools and documentation search. Related: Embedding, RAG (Retrieval-Augmented Generation)
Use glossary context, relationships, mental models, and builder paths to get structured answers instead of generic chat output.
Optional: paste Anchor, Solana, or Rust code so the Copilot can map primitives back to glossary terms.
The Copilot will answer using the current term, related concepts, mental models, and the surrounding glossary graph.
These branches show which concepts this term touches directly and what sits one layer beyond them.
A dense vector representation of text (or other data) in a continuous high-dimensional space where semantically similar items are closer together. Embedding models (OpenAI ada-002, Cohere, sentence-transformers) convert text to vectors of 256-3072 dimensions. Used in RAG for semantic search, in recommendation systems, and for clustering. Stored and queried via vector databases.
An AI architecture that combines LLMs with external knowledge retrieval. Instead of relying solely on training data, RAG systems retrieve relevant documents from a knowledge base (using embeddings and vector search), then provide them as context to the LLM. RAG reduces hallucinations and enables up-to-date responses. Useful for blockchain documentation bots and developer assistants.
These are the next concepts worth opening if you want this term to make more sense inside a real Solana workflow.
Glossary entries become useful when they are connected. These links are the shortest path to adjacent ideas.
A dense vector representation of text (or other data) in a continuous high-dimensional space where semantically similar items are closer together. Embedding models (OpenAI ada-002, Cohere, sentence-transformers) convert text to vectors of 256-3072 dimensions. Used in RAG for semantic search, in recommendation systems, and for clustering. Stored and queried via vector databases.
An AI architecture that combines LLMs with external knowledge retrieval. Instead of relying solely on training data, RAG systems retrieve relevant documents from a knowledge base (using embeddings and vector search), then provide them as context to the LLM. RAG reduces hallucinations and enables up-to-date responses. Useful for blockchain documentation bots and developer assistants.
Use these paths when you want to move from isolated lookup into guided exploration.
Map the glossary to the agentic workflow and context retrieval stack.
These entries live beside the current term and help the page feel like part of a larger knowledge graph instead of a dead end.
A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.
The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5).
A neural network component that allows models to weigh the relevance of different parts of the input when producing output. Self-attention computes query-key-value dot products across all positions, enabling each token to 'attend' to every other token. Multi-head attention runs multiple attention functions in parallel. Attention is O(n²) in sequence length, driving context window research.
A large AI model trained on broad data that can be adapted for many downstream tasks. Foundation models (GPT-4, Claude, Llama 3, Gemini) are pre-trained on internet-scale text/code and can be fine-tuned, prompted, or used via APIs for specific applications. The term emphasizes that one base model serves as the foundation for diverse use cases rather than training task-specific models.